RDBMS to Graph Migration

2.1 RDBMS vs. Graph Databases

• RDBMS uses tables to represent data, while graph databases use nodes and edges.
• Relationships in RDBMS are represented through foreign keys; in graph databases, they are first-class citizens.
• Graph databases typically provide more efficient traversals for complex queries involving relationships.

2.2 Data Modeling

Understanding how to model data is critical when migrating from RDBMS to a graph database:

• Identify entities as nodes.
• Identify relationships as edges.
• Define properties for nodes and edges.

3.1 Step-by-Step Process

graph TD;
    A[Extract Data] --> B[Transform Data];
    B --> C[Load Data];
            

The migration process can be broken down into three main steps:

1. Extract Data: Retrieve data from the RDBMS using SQL queries.
2. Transform Data: Convert the extracted data into a graph structure.
3. Load Data: Insert the transformed data into the graph database.

For example, if using Python:

import py2neo

# Connect to graph database
graph = py2neo.Graph("bolt://localhost:7687", auth=("username", "password"))

# Example of creating nodes and relationships
graph.run("CREATE (a:Person {name: 'Alice'})")
graph.run("CREATE (b:Person {name: 'Bob'})")
graph.run("MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'}) "
          "CREATE (a)-[:KNOWS]->(b)")
            

4.1 Data Integrity

Ensure that relationships are accurately captured during migration.

4.2 Performance Optimization

Optimize queries for graph traversals post-migration.

4.3 Test Migration

Always test the migration process on a smaller dataset before full implementation.